Yuval et al. [1]} demonstrated the use of online and offline Random Forests to learn a
parameterization from the coarse-grained output of a three-dimensional high-resolution idealized atmospheric
model. In their experiment, numerical precision is low (16 bits) and the classifiers are trained across resolutions.
We note from their work that online random forests have more stable performance across resolutions than offline ones. This could indicate our observed performance stability across numerical precisions is correlated characteristics of online algorithms.
